{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# （2）- 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入相关包\n",
    "%matplotlib inline\n",
    "# 绘图包\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#定义文件名\n",
    "ACTION_201602_FILE = \"data/JData_Action_201602.csv\"\n",
    "ACTION_201603_FILE = \"data/JData_Action_201603.csv\"\n",
    "ACTION_201604_FILE = \"data/JData_Action_201604.csv\"\n",
    "COMMENT_FILE = \"data/JData_Comment.csv\"\n",
    "PRODUCT_FILE = \"data/JData_Product.csv\"\n",
    "USER_FILE = \"data/JData_User.csv\"\n",
    "USER_TABLE_FILE = \"data/User_table.csv\"\n",
    "ITEM_TABLE_FILE = \"data/Item_table.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 周一到周日各天购买情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 提取购买(type=4)的行为数据\n",
    "def get_from_action_data(fname, chunk_size=50000):\n",
    "    reader = pd.read_csv(fname, header=0, iterator=True)\n",
    "    chunks = []\n",
    "    loop = True\n",
    "    while loop:\n",
    "        try:\n",
    "            chunk = reader.get_chunk(chunk_size)[\n",
    "                [\"user_id\", \"sku_id\", \"type\", \"time\"]]\n",
    "            chunks.append(chunk)\n",
    "        except StopIteration:\n",
    "            loop = False\n",
    "            print(\"Iteration is stopped\")\n",
    "\n",
    "    df_ac = pd.concat(chunks, ignore_index=True)\n",
    "    # type=4,为购买\n",
    "    df_ac = df_ac[df_ac['type'] == 4]\n",
    "\n",
    "    return df_ac[[\"user_id\", \"sku_id\", \"time\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration is stopped\n",
      "Iteration is stopped\n",
      "Iteration is stopped\n"
     ]
    }
   ],
   "source": [
    "df_ac = []\n",
    "df_ac.append(get_from_action_data(fname=ACTION_201602_FILE))\n",
    "df_ac.append(get_from_action_data(fname=ACTION_201603_FILE))\n",
    "df_ac.append(get_from_action_data(fname=ACTION_201604_FILE))\n",
    "df_ac = pd.concat(df_ac, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_id     int64\n",
      "sku_id      int64\n",
      "time       object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_ac.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将time字段转换为datetime类型\n",
    "df_ac['time'] = pd.to_datetime(df_ac['time'])\n",
    "\n",
    "# 使用lambda匿名函数将时间time转换为星期(周一为1, 周日为７)\n",
    "df_ac['time'] = df_ac['time'].apply(lambda x: x.weekday() + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>sku_id</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>269365</td>\n",
       "      <td>166345</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>235443</td>\n",
       "      <td>36692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>247689</td>\n",
       "      <td>9112</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>273959</td>\n",
       "      <td>102034</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>226791</td>\n",
       "      <td>163550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  sku_id  time\n",
       "0   269365  166345     1\n",
       "1   235443   36692     1\n",
       "2   247689    9112     1\n",
       "3   273959  102034     1\n",
       "4   226791  163550     1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 周一到周日每天购买用户个数\n",
    "df_user = df_ac.groupby('time')['user_id'].nunique()\n",
    "df_user = df_user.to_frame().reset_index()\n",
    "df_user.columns = ['weekday', 'user_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 周一到周日每天购买商品个数\n",
    "df_item = df_ac.groupby('time')['sku_id'].nunique()\n",
    "df_item = df_item.to_frame().reset_index()\n",
    "df_item.columns = ['weekday', 'item_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 周一到周日每天购买记录个数\n",
    "df_ui = df_ac.groupby('time', as_index=False).size()\n",
    "df_ui = df_ui.to_frame().reset_index()\n",
    "df_ui.columns = ['weekday', 'user_item_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x215daabeef0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tLF26lPLycg444ABKSkpo167ueEp8YnrHjh2bvc/l5eWcddZZTJ8+fZe+H3LI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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215ca80a208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "\n",
    "plt.bar(df_user['weekday'], df_user['user_num'], bar_width, \n",
    "        alpha=opacity, color='c', label='user')\n",
    "plt.bar(df_item['weekday']+bar_width, df_item['item_num'], \n",
    "        bar_width, alpha=opacity, color='g', label='item')\n",
    "plt.bar(df_ui['weekday']+bar_width*2, df_ui['user_item_num'], \n",
    "        bar_width, alpha=opacity, color='m', label='user_item')\n",
    "\n",
    "plt.xlabel('weekday')\n",
    "plt.ylabel('number')\n",
    "plt.title('A Week Purchase Table')\n",
    "plt.xticks(df_user['weekday'] + bar_width * 3 / 2., (1,2,3,4,5,6,7))\n",
    "plt.tight_layout() \n",
    "plt.legend(prop={'size':10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：周六，周日购买量较少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个月中各天购买量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2016年2月"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration is stopped\n"
     ]
    }
   ],
   "source": [
    "df_ac = get_from_action_data(fname=ACTION_201602_FILE)\n",
    "\n",
    "# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天\n",
    "df_ac['time'] = pd.to_datetime(df_ac['time']).apply(lambda x: x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>sku_id</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>269365</td>\n",
       "      <td>166345</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>235443</td>\n",
       "      <td>36692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>247689</td>\n",
       "      <td>9112</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>273959</td>\n",
       "      <td>102034</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>226791</td>\n",
       "      <td>163550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  sku_id  time\n",
       "351    269365  166345     1\n",
       "649    235443   36692     1\n",
       "980    247689    9112     1\n",
       "1719   273959  102034     1\n",
       "2153   226791  163550     1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>sku_id</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11464511</th>\n",
       "      <td>256461</td>\n",
       "      <td>126092</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11470852</th>\n",
       "      <td>224347</td>\n",
       "      <td>137636</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11478541</th>\n",
       "      <td>300214</td>\n",
       "      <td>102335</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11480871</th>\n",
       "      <td>213442</td>\n",
       "      <td>48000</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11483928</th>\n",
       "      <td>228994</td>\n",
       "      <td>165190</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  sku_id  time\n",
       "11464511   256461  126092    29\n",
       "11470852   224347  137636    29\n",
       "11478541   300214  102335    29\n",
       "11480871   213442   48000    29\n",
       "11483928   228994  165190    29"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ac.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_user = df_ac.groupby('time')['user_id'].nunique()\n",
    "df_user = df_user.to_frame().reset_index()\n",
    "df_user.columns = ['day', 'user_num']\n",
    "\n",
    "df_item = df_ac.groupby('time')['sku_id'].nunique()\n",
    "df_item = df_item.to_frame().reset_index()\n",
    "df_item.columns = ['day', 'item_num']\n",
    "\n",
    "df_ui = df_ac.groupby('time', as_index=False).size()\n",
    "df_ui = df_ui.to_frame().reset_index()\n",
    "df_ui.columns = ['day', 'user_item_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x215da7dcba8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jPCPJZUkeSXJjt+SKJG8ppXwryfYkb0tyb5JPJLsXK/tAkneXUnYk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\nS80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215bcd6c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "# 天数\n",
    "day_range = range(1,len(df_user['day']) + 1, 1)\n",
    "# 设置图片大小\n",
    "plt.figure(figsize=(14,10))\n",
    "\n",
    "plt.bar(df_user['day'], df_user['user_num'], bar_width, \n",
    "        alpha=opacity, color='c', label='user')\n",
    "plt.bar(df_item['day']+bar_width, df_item['item_num'], \n",
    "        bar_width, alpha=opacity, color='g', label='item')\n",
    "plt.bar(df_ui['day']+bar_width*2, df_ui['user_item_num'], \n",
    "        bar_width, alpha=opacity, color='m', label='user_item')\n",
    "\n",
    "plt.xlabel('day')\n",
    "plt.ylabel('number')\n",
    "plt.title('February Purchase Table')\n",
    "plt.xticks(df_user['day'] + bar_width * 3 / 2., day_range)\n",
    "# plt.ylim(0, 80)\n",
    "plt.tight_layout() \n",
    "plt.legend(prop={'size':9})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析： 2月份5,6,7,8,9,10 这几天购买量非常少，原因可能是中国农历春节，快递不营业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2016年3月"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration is stopped\n"
     ]
    }
   ],
   "source": [
    "df_ac = get_from_action_data(fname=ACTION_201603_FILE)\n",
    "\n",
    "# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天\n",
    "df_ac['time'] = pd.to_datetime(df_ac['time']).apply(lambda x: x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_user = df_ac.groupby('time')['user_id'].nunique()\n",
    "df_user = df_user.to_frame().reset_index()\n",
    "df_user.columns = ['day', 'user_num']\n",
    "\n",
    "df_item = df_ac.groupby('time')['sku_id'].nunique()\n",
    "df_item = df_item.to_frame().reset_index()\n",
    "df_item.columns = ['day', 'item_num']\n",
    "\n",
    "df_ui = df_ac.groupby('time', as_index=False).size()\n",
    "df_ui = df_ui.to_frame().reset_index()\n",
    "df_ui.columns = ['day', 'user_item_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x215bf7ba278>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KCLcAAAAAAJURbgEAAAAAKiPcAgAAAABURrgFAAAAAKiM\ncAsAAAAAUBnhFgAAAACgMsItAAAAAEBlhFsAAAAAgMo8Ou0JcLmapknbtqeOLy8vp9/vX+KMAAAA\nAICjhNsF0jRNNq9vZjwcn3rN0tpSNq5tiLcAAAAAMEXC7QJp2zbj4TjrK+sZ9AbHxkfbo2wNt9K2\nrXALAAAAAFMk3C6gQW+Q1f7qyYM7lzsXAAAAAOA4h5MBAAAAAFRGuAUAAAAAqIxwCwAAAABQGeEW\nAAAAAKAywi0AAAAAQGWEWwAAAACAygi3AAAAAACVEW4BAAAAACoj3AIAAAAAVEa4BQAAAACojHAL\nAAAAAFAZ4RYAAAAAoDLCLQAAAABAZYRbAAAAAIDKCLcAAAAAAJURbgEAAAAAKiPcAgAAAABURrgF\nAAAAAKiMcAsAAAAAUBnhFgAAAACgMsItAAAAAEBlhFsAAAAAgMoItwAAAAAAlRFuAQAAAAAqI9wC\nAAAAAFRGuAUAAAAAqIxwCwAAAABQGeEWAAAAAKAywi0AAAAAQGWEWwAAAACAygi3AAAAAACVEW4B\nAAAAACoj3AIAAAAAVEa4BQAAAACoTBXhtpTyVaWUny6lvFJKuVtK+dMnXPM9pZRXSynbpZSfKaW8\n+cj4UinlPaWUT5ZSPltKeX8p5Ykj1zxeStkspYxKKZ8upfxwKeX1F/31AQDwcJqmySc/+ckT35qm\nmfb0AADg3D067Qnse32SjyZ5X5J/enSwlPK3kvzVJN+c5DeTfG+SD5ZSvqjrut39y96d5OuSfH2S\nzyR5T5KfSPJVBz7UjyV5Y5JnkrwuyT9K8kNJvum8vyAAAM5H0zTZvL6Z8XB84vjS2lI2rm2k3+9f\n8swAAODiVBFuu677QJIPJEkppZxwybcneWfXdf98/5pvTvKJJH8myY+XUt6Q5G1JvqHrug/tX/Mt\nSX61lPIVXdd9pJTyRUn+WJL1rute2r/m7Un+RSnlb3Zd9/GL/SoBAJhE27YZD8dZX1nPoDc4NDba\nHmVruJW2bYVbAADmShVbJZyllPIFSZ5M8nOvPdZ13WeS/GKSt+4/9OXZi9AHr/n1JL994JqvTPLp\n16Ltvp9N0iV5y0XNHwCA8zHoDbLaXz30djTkAgDAvKg+3GYv2nbZW2F70Cf2x5K97Q9294Puadc8\nmeTmwcGu6z6X5FMHrgEAAAAAmLpZCLcAAAAAAAulij1u7+HjSUr2VtUeXHX7xiQvHbjmdaWUNxxZ\ndfvG/bHXrnni4AcupXxektUD15zo2WefzWBw+Da8q1ev5urVqw/2lQAAAAAAC+HGjRu5cePGocdG\no9F9v3/14bbruo+VUj6e5Jkkv5wk+4eRvSXJe/Yv20pyZ/+an9y/5guTvCnJh/ev+XCSx0opX3Zg\nn9tnsheFf/GsOTz33HN5+umnz+1rAgAAAADm20kLP1988cWsr6/f1/tXEW5LKa9P8ubsRdQk+cOl\nlC9N8qmu634nybuTfGcp5TeS/GaSdyb5z0l+Ktk7rKyU8r4k7yqlfDrJZ5P8QJKf77ruI/vX/Fop\n5YNJ/kEp5VuTvC7JDya50XXdmStuAQAAAAAuUxXhNsmXJ/k32TuErEvy/fuP/0iSt3Vd93dLKb0k\nP5TksSTPJ/m6rut2D3yMZ5N8Lsn7kywl+UCSbzvyeb4xyd9P8rNJ7u5f++0X8QUBAAAAAEyqinDb\ndd2Hco+D0rque0eSd5wxPk7y9v230675vSTfNNEkAQAAAAAuyZmxFAAAAACAyyfcAgAAAABURrgF\nAAAAAKiMcAsAAAAAUBnhFgAAAACgMsItAAAAAEBlhFsAAAAAgMoItwAAAAAAlRFuAQAAAAAqI9wC\nAAAAAFTm0WlPAACAy7U7Hmc4HJ44NhwOs7u7e8kzAgAAjhJuAQAWyHh7Oy+9+ELee+vV9Hq9Y+Pb\nt7Zz8+WbufL4laQ/hQkCAABJhFsAgIVyezzOzp1bWXlqJWtPrh0bv/vK3ew8v5Pbd25PYXYAAMBr\nhFsAgAW03F9O/7HjS2qbUTOF2QAAAEc5nAwAAAAAoDJW3ALAfWqaJm3bnji2vLycft+GoAAAAJwP\n4RYA7kPTNNm8vpnxcHzi+NLaUjaubYi3AAAAnAvhFgDuQ9u2GQ/HWV9Zz6A3ODQ22h5la7iVtm2F\nWwAAAM6FcAsAD2DQG2S1v3p8YOfy5wIAAMD8cjgZAAAAAEBlrLidQWcdjpM4IAcAAAAAZp1wO2Oa\npsn1zc0MxycfjpMka0tLubbhgBwAAAAAmFXC7Yxp2zbD8Tgr6+vpDQbHxrdHowy3HJADAAAAALNM\nuJ1RvcEg/dUTDseJ83EAAAAAYNY5nAwAAAAAoDLCLQAAAABAZYRbAAAAAIDKCLcAAAAAAJURbgEA\nAAAAKiPcAgAAAABURrgFAAAAAKiMcAsAAAAAUBnhFgAAAACgMsItAAAAAEBlhFsAAAAAgMoItwAA\nAM+QezkAACAASURBVAAAlRFuAQAAAAAq8+i0JwAAwGJomiZt2546vry8nH6/f4kzAgCAegm3AABc\nuKZpcn1zM8Px+NRr1paWcm1jQ7wFAIAItwAAXIK2bTMcj7Oyvp7eYHBsfHs0ynBrK23bCrcAABDh\nFgCAS9QbDNJfXT1xbOeS5wKwCGxTAzC7hFsAAACYQ03TZPP6ZsbD07epWVpbysY129QA1Ei4BQAA\ngDnUtm3Gw3HWV9Yz6B3fpma0PcrW0DY1ALUSbgEAAGCODXqDrPZP3qbGPjUA9Xpk2hMAAAAAAOAw\n4RYAAAAAoDLCLQAAAABAZYRbAAAAAIDKCLcAAAAAAJURbgEAAAAAKvPotCcAAEyuaZq0bXvq+PLy\ncvr9/iXOCAAAgPMg3ALAjGqaJpvXNzMejk+9ZmltKRvXNsRbAACAGSPcAsCMats24+E46yvrGfQG\nx8ZH26NsDbfStq1wCwAAMGOEWwCYcYPeIKv91ZMHdy53LgAAAJwPh5MBAAAAAFRGuAUAAAAAqIxw\nCwAAAABQGeEWAAAAAKAywi0AAAAAQGWEWwAAAACAygi3AAAAAACVEW4BAAAAACrz6LQnAACXqWma\ntG174tjy8nL6/f4lzwgAAACOE24BWBhN0+T65maG4/GJ42tLS7m2sSHeAgAAMHXCLQALo23bDMfj\nrKyvpzcYHBrbHo0y3NpK27bCLQAAAFMn3AKwcHqDQfqrq8ce35nCXAAAAOAkDicDAAAAAKiMFbcA\nC85hXQAAAFAf4RZggTVNk83rmxkPTz6sa2ltKRvXHNYFAAAAl024BVhgbdtmPBxnfWU9g97hw7pG\n26NsDR3WBQAAANMg3AKQQW+Q1f7xw7qc1gUAAADT4XAyAAAAAIDKCLcAAAAAAJWxVQIAAAAA961p\nmrRte+LY8vKyMzLgnAi3AAAAANyXpmmyeX0z4+H4xPGltaVsXNsQb+EcCLcAAAAA3Je2bTMejrO+\nsp5Bb3BobLQ9ytZwK23bCrdwDoRbAAAAAB7IoDfIan/1+MDO5c8F5pXDyQAAAAAAKmPFLQAAAFTs\nrIOgEodBAcwr4RYAAAAq1TRNrm9uZjg++SCoJFlbWsq1DYdBAcwb4RYAAAAq1bZthuNxVtbX0xsM\njo1vj0YZbjkMCmAeCbcAAABQud5gkP7qCQdBxVlQAPPK4WQAAAAAAJURbgEAAAAAKmOrBJghTpMF\nZoGfVQAAAA9PuIUZ0TRNNq9vZjw8/TTZpbWlbFxzmiwwPX5WAQAAnA/hFmZE27YZD8dZX1nPoHf8\nNNnR9ihbQ6fJAtPlZxUAAMD5EG5hxgx6g6z2Tz5N1nGyQC38rAIAAHg4DicDAAAAAKiMFbdU4ayD\nbBxiA8w7h3kBAHAar5dhcQm3TN29DrJxiA0wz5qmyfXNzQzHpx/mtba0lGsbfg7Oqt3xOMPh8Njj\nw+Ewu7u7U5gRADArvF6GxSbcMnVnHWTjEBtg3rVtm+F4nJX19fQGxw/z2h6NMtzyc3BWjbe389KL\nL+S9t15Nr9c7NLZ9azs3X76ZK49fSXxrAYATeL0Mi024pRqnHmTjEBtgAfQGg/RXTz7My4/B2XV7\nPM7OnVtZeWola0+uHRq7+8rd7Dy/k9t3bk9pdgDArPB6eXbZ6oKHIdwCAFyw5f5y+o8dflLejJop\nzQYAgMtgqwselnALAAAAAOfMVhc8LOEWAAAAAC6IrS6Y1CPTngAAAAAAAIcJtwAAAAAAlbFVAgAA\nQCWcPg4AvEa4BQAAqIDTxwGAg4RbAACACjh9HAA4SLgFAACoiNPHAYDE4WQAAAAAANWx4hYAAOaI\nw60AAOaDcAunOOtFT+KFDwBQH4dbAXC//KIP6ifcwgnu9aIn8cIHAKiPw60AuB9N0+T65maG45Nf\n864tLeXahte7MG3CLZzgrBc9iRc+AHARdsfjDIfDY48Ph8Ps7u5OYUazy+FWAJylbdsMx+OsrK+n\nNzj8mnd7NMpwy+tdqIFwC2c49UVP4oUPAJyj8fZ2Xnrxhbz31qvp9XqHxrZvbefmyzdz5fErideP\nAHBueoNB+qvHX/N6uQt1EG4BAJi62+Nxdu7cyspTK1l7cu3Q2N1X7mbn+Z3cvnN7SrMDAIDLJ9wC\nAFCN5f5y+o8dXlbbjJopzQYAAKbnkWlPAAAAAACAw4RbAAAAAIDK2CoBYA40TZO2bU8cW15edhos\nAMAl8JwMgPMk3ALMuKZpcn1zM8Px+MTxtaWlXNvY8EIBAOACNU2TzeubGQ9Pfk62tLaUjWuekwFw\n/4RbgHO0Ox5nOByeODYcDrO7u3vun7Nt2wzH46ysr6c3GBwa2x6NMtzaStu2M/Ui4azVKokVKwBA\nfdq2zXg4zvrKega9w8/JRtujbA1n7zkZANMl3AKck/H2dl568YW899ar6fV6x8a3b23n5ss3c+Xx\nK8kFPF/vDQbpr64ee3zn/D/VhbrXapXEihUAoF6D3iCr/ePPyWbuSRkAUyfcApyT2+Nxdu7cyspT\nK1l7cu3Y+N1X7mbn+Z3cvnN7CrObHWetVkmsWAEAAGAxCLcA52y5v5z+Y8eDYjNqpjCb2XXqapXE\nipVL4oAVAADwvJjpEW4BgGMcegcAAJ4XM13CLTCXHG4FD2ceD70DAIAH5Xkx0yTcAnPH4VZwfubl\n0DsAAObbRS/e8byYaRBugbnjcCsAAIDFYfEO80q4BeaWw60AAADmn8U7zCvhFgAAAICZZ/EO8+aR\naU8AAAAAAIDDrLiFKThr0/SH3TAdAACAungNCExCuIVL1jRNrm9uZjg+edP0taWlXNuYrQ3TPQkB\nAAA42Ty+BgQuh3ALl6xt2wzH46ysr6c3OLxp+vZolOHWbG2Yfq/TO53cCQAALLJ5ew0IXB7hFqak\nNxikv3p80/RZ2y/9rNM7ndwJADD7zrq7KnGHFdyveXkNCFwe4ZZz43b5xXbq6Z2ehQAwp8QsFsG9\n7q5K3GEFABdFuJ1Du+NxhsPhsceHw2F2d3cv5HPaswcAWCRiFovirLurEndYAcBFEm7nzHh7Oy+9\n+ELee+vV9Hq9Q2Pbt7Zz8+WbufL4leScn1PZswcAWCRiFmeZxzvRTr27KnGHFQBcEOF2ztwej7Nz\n51ZWnlrJ2pNrh8buvnI3O8/v5Pad2xf2+e3ZA0Ct5jGkMH1iFke5Ew0AOC/C7Zxa7i+n/9jhJ4PN\nqJnSbKbH3nMAJPe+rd0t7cB5cScaAHBehFvm1r1WOyRWPAAsirNua3dLO3AR3IkGADws4Za5ddZq\nh8SKB4BFdOpt7UoKAABQGeGWuXfaaofE63QAoE7ztiez7asAAB6ccAsAABWZt8Ot7rXHdGKfaQCA\nkwi3AABQkXk73OqsPaYT+0wDAJxGuAUAgArN2+FWp+4xnczuFwUAcIEemfYE7kcp5btLKXePvP2H\nI9d8Tynl1VLKdinlZ0opbz4yvlRKeU8p5ZOllM+WUt5fSnnicr8SAAAAAIB7m4lwu+9XkrwxyZP7\nb//DawOllL+V5K8muZbkK5LcSvLBUsrrDrz/u5P8ySRfn+Srk/yBJD9xKTMHAAAAAHgAs7RVwp2u\n6373lLFvT/LOruv+eZKUUr45ySeS/JkkP15KeUOStyX5hq7rPrR/zbck+dVSyld0XfeRi58+AAAA\nAMD9maVw+1Qp5ZUkbZIPJ/lfu677nVLKF2RvBe7PvXZh13WfKaX8YpK3JvnxJF+eva/14DW/Xkr5\n7f1rhFsAAABYYE3TpG3bU8eXl5cdoghcqlkJt7+Q5C8k+fUkn5/kHUn+XSnli7MXbbvsrbA96BP7\nY8neFgu7Xdd95oxrAAAAgAXUNE02r29mPByfes3S2lI2rm2It8ClmYlw23XdBw/881dKKR9J8ltJ\n/nySX5vOrAAAAIBJnLW6dRorW9u2zXg4zvrKega9wbHx0fYoW8OttG0r3AKXZibC7VFd141KKf8x\nyZuT/NskJXurag+uun1jkpf2//7xJK8rpbzhyKrbN+6PnenZZ5/NYHD4B/fVq1dz9erVib8GAAAA\nWET3Wt06zZWtg94gq/3Vkwd3LncuwOy7ceNGbty4ceix0Wh03+8/k+G2lNLPXrT9ka7rPlZK+XiS\nZ5L88v74G5K8Jcl79t9lK8md/Wt+cv+aL0zypuztl3um5557Lk8//fR5fxkAc80eYQDARaptxSb3\n76zVrVa2AvPkpIWfL774YtbX1+/r/Wci3JZS/l6Sf5a97RH+YJK/k+R2kn+yf8m7k3xnKeU3kvxm\nkncm+c9Jfir5/cPK3pfkXaWUTyf5bJIfSPLzXdc5mAzgnDVNk+ubmxmOT98jbG1pKdc26tojbHc8\nznA4PHFsOBxmd3f3kmcEAJyk5hWb3L9TV7da2QqQZEbCbZI/lOTHkqwl+d0k/2eSr+y6bpgkXdf9\n3VJKL8kPJXksyfNJvq7ruoOvsJ9N8rkk70+ylOQDSb7t0r4CgAXStm2G43FW1tfTGxzfI2x7NMpw\nq66VFOPt7bz04gt5761X0+v1jo1v39rOzZdv5srjV5I6pgwAC+siV2y6awiAWsxEuO267p6byXZd\n944k7zhjfJzk7ftvAFyC3mCQ/urJe4TVtpDi9nicnTu3svLUStaeXDs2fveVu9l5fie379y+9Lmd\nthLYKmAAFt15r9ic1buGAJhPMxFuAeCyLPeX03/s+AuxZtRMYTZnrwS2ChgAztcs3jUE88SKdzhM\nuAWSONwBanXWSuBprgIGgHk2S3cNJe7OYT5Y8Q7HCbfAPf+DnNZ/jn7bCv+/k1YCT2sVMABQD3fn\nMC+seIfjhFvgzP8gp/Wfo9+2AgCcH78Qn1/uzmHezNqKd7hIwi3w+077D3Ia/zn6bSsAwPnwC/HF\n4O4cgPkj3AJV89tWAICH4xfiADCbhFuAOXfaYRWJAysA3D7OIvELcQCYLcItwBw767CKxIEVwGJz\n+zgAADUTbgHm2FmHVSQOrAAWm9vHF5vV1gBA7YRbgAVw0mEViQMrABK3jy+ipmmyeX0z4+Hpq62X\n1paycc1qawBgeoRbAABgobRtm/FwnPWV9Qx6x1dbj7ZH2RpabQ0ATJdwCwDMjLNubXZbM/CgBr1B\nVvsnr7a23BoAmDbhFgCYCfc6SMohUgAAwDwRbrkUu+NxhsPhiWPD4TC7u7uXPCMAZs1ZB0k5RAoA\nAJg3wi0Xbry9nZdefCHvvfVqer3esfHtW9u5+fLNXHn8SuK1NgD3cNpBUu5qBpg9tsABgNMJt1y4\n2+Nxdu7cyspTK1l7cu3Y+N1X7mbn+Z3cvnN7CrMDAACmwRY4AHA24ZZLs9xfTv+x40+6mlEzhdnU\n67RtJWwpAQDAPLEFDgCcTbiFipy1rYQtJQAAmEe2wGHR1XgmTI1zgkUk3EJFztpWwpYSAEDixTTA\nPKnxTJga5wSLSriFCp20rYQtJQAAL6YB5kuNZ8LUOCdYVMItAADMiGm+mG6aJm3bnjq+vLxsL1KA\nCdV4JkyNc4JFI9wCAMCMuewX003T5PrmZobj8anXrC0t5drGhngLTJVfMgHzRLgFYCpO26PR/oyz\nwfcPFkvbthmOx1lZX09vMDg2vj0aZbi1lbZtBRFYIGdF0mkEUr9kYhrsPc9FEm4BuHRn7dFof8b6\n+f7B4uoNBumvrp44tnPJcwGm616RdBqB1C+Z5l9tiwfsPc9FE24BuHRn7dHosIP6+f4BAGdF0mkH\nUr9kmk81Lh5wkBsXTbgFYGpO2qPRYQezw/cPgEVU24q/aTstkgqknLeaFw84yI2LItwCAADAfahx\nxR8sGosHWCTCLQAAANyHmlf8ATB/hFsWmtucZpvvH3CQE32BWeBn1Xyw4g+AyyDcsrDc5jTbfP+A\ng5zoC8wCP6sAgAch3LKw3OY023z/gIOc6AvMAj+rAIAHIdyy8NzmNNt8/4CDnOgLzAI/qwCA+yHc\nAgDAKeynXr+madK27Yljy8vL6fftOQAAzCbhFgAATmA/9fo1TZPrm5sZjscnjq8tLeXaxoZ4CwDM\nJOEWAABOYD/1+rVtm+F4nJX19fQGg0Nj26NRhltbadtWuJ1Rp614T6x6B2AxCLcAAHAG+6nfn2lu\nK9EbDNJfXT32+M6Fftb5Utu2IGeteE+segdgMQi3AADAQ7GtxGyr8ft31or3xKp3ABaDcAsAADwU\n20rMtpq/fyeteE+segdgMQi3wD3ZXwwAuB+2lZhtvn8AUBfhFjiT/cUAAIAHVevij9r2cwY4i3AL\nnMn+YgAAwIOodfFHjfs5A5xFuAXui/3FAACA+1Hr4o+a93MGOIlwCwAAAJy7Whd/2M8ZmBWPTHsC\nAAAAAAAcJtwCAAAAAFRGuAUAAAAAqIw9bgEAgLm0Ox5nOBwee3w4HGZ3d3cKMwIAuH/CLQAAMHfG\n29t56cUX8t5br6bX6x0a2761nZsv38yVx68kx89NAgCognALAADMndvjcXbu3MrKUytZe3Lt0Njd\nV+5m5/md3L5ze0qzAwC4N+EWAACYW8v95fQfO7ysthk1U5oNAMD9czgZAAAAAEBlhFsAAAAAgMoI\ntwAAAAAAlRFuAQAAAAAqI9wCAAAAAFRGuAUAAAAAqIxwCwAAAABQGeEWAAAAAKAywi0AAAAAQGWE\nWwAAAACAygi3AAAAAACVEW4BAAAAACoj3AIAAAAAVEa4BQAAAACojHALAAAAAFAZ4RYAAAAAoDLC\nLQAAAABAZYRbAAAAAIDKCLcAAAAAAJURbgEAAAAAKiPcAgAAAABURrgFAAAAAKiMcAsAAAAAUBnh\nFgAAAACgMsItAAAAAEBlhFsAAAAAgMoItwAAAAAAlRFuAQAAAAAqI9wCAAAAAFRGuAUAAAAAqIxw\nCwAAAABQGeEWAAAAAKAywi0AAAAAQGWEWwAAAACAygi3AAAAAACVEW4BAAAAACoj3AIAAAAAVEa4\nBQAAAACojHALAAAAAFAZ4RYAAAAAoDLCLQAAAABAZYRbAAAAAIDKCLcAAAAAAJURbgEAAAAAKiPc\nAgAAAABURrgFAAAAAKiMcAsAAAAAUBnhFgAAAACgMsItAAAAAEBlhFsAAAAAgMoItwAAAAAAlRFu\nAQAAAAAqI9wCAAAAAFRGuAUAAAAAqIxwCwAAAABQGeEWAAAAAKAywi0AAAAAQGWEWwAAAACAygi3\nAAAAAACVEW4BAAAAACoj3AIAAAAAVEa4BQAAAACojHALAAAAAFAZ4RYAAAAAoDLCLQAAAABAZYRb\nAAAAAIDKCLcAAAAAAJURbgEAAAAAKiPcAgAAAABURrgFAAAAAKiMcAsAAAAAUBnhFgAAAACgMsIt\nAAAAAEBlhFsAAAAAgMoItwAAAAAAlRFuAQAAAAAqI9wCAAAAAFRGuAUAAAAAqIxwCwAAAABQGeEW\nAAAAAKAywi0AAAAAQGWEWwAAAACAygi3AAAAAACVEW4BAAAA/r/27j1YkrI+4/j3hwtsAC9E5KKC\nERUhpYCAGo0giqBRxKJCFMEooEbiBQImIKUGlSQqGkQlqKUgIKKiomAVgiDGIEQXIUFBFlBYWa7h\nsivssiuX/eWPt89ymJ1zptdy+u1z9vupmipO95yzD909z3S/090jST3jwK0kSZIkSZIk9YwDt5Ik\nSZIkSZLUMw7cSpIkSZIkSVLPOHArSZIkSZIkST3jwK0kSZIkSZIk9YwDt5IkSZIkSZLUMw7cSpIk\nSZIkSVLPrHEDtxHxroi4MSKWRcRPI+L5tTON27zz5tWOsAoztdfHXGZqr4+5+pgJ+pnLTO31MZeZ\n2utjrj5mgn7mMlN7fczVx0zQz1xmaq+PuczUXh9z9TET9DNXHzNp5lqjBm4j4g3AvwNHA88DrgTO\nj4iNqgYbs8vOv6x2hFWYqb0+5jJTe33M1cdM0M9cZmqvj7nM1F4fc/UxE/Qzl5na62OuPmaCfuYy\nU3t9zGWm9vqYq4+ZoJ+5+phJM9caNXALHAZ8ITNPy8z5wMHA/cBBdWNJkiRJkiRJ0iPWmIHbiFgb\n2BH44cS0zEzgQuBFtXJJkiRJkiRJ0qA1ZuAW2Ah4DHDHwPQ7gE27jyNJkiRJkiRJw82pHaDn5gJc\nc801tXOstGjRIm5fuJBF8+Yxd4MNVp1/220sWXQvC365gMW3LQZgyeIlzL9sPov+bxGL71vMFQuu\n4KbFN63yu0uWL2Hh7xdy5ZVXsuGGG/7Rck2XCZg217gyDcvVNtM4c7n+2mUalqvv629UrjUp06hc\nrr+B+TNsW+9jJnD9TZcJZvaysqtm9vobZy7XX7tMo3K5rAbm21Wtcs229TfOXH1cVn3MNCrXmrb+\nNLNNGmecO+q5Ue4WMPs1t0q4H/jrzDxn0vRTgMdn5t5Dfmc/4KudhZQkSZIkSZK0Jtg/M8+Y7glr\nzBm3mflgRFwO7AacAxAR0fz8mSl+7Xxgf2ABsLyDmJIkSZIkSZJmr7nAn1HGHae1xpxxCxARrwdO\nAQ4G5gGHAfsAW2fmnRWjSZIkSZIkSdJKa8wZtwCZeWZEbAR8BNgE+F/glQ7aSpIkSZIkSeqTNeqM\nW0mSJEmSJEmaCdaqHUCSJEmSJEmS9GgO3M5SEbFzRJwTEbdExIqI2KsHmY6KiHkRcW9E3BER34mI\nrSpnOjgiroyI3zWPSyPiVTUzDYqI9zXr8LjKOY5uckx+/KpmpibXkyPiKxFxV0Tc36zPHSpnunHI\nsloREZ+tmGmtiDgmIm5oltOvI+IDtfJMyrVBRBwfEQuaXD+JiJ06zjCyLyPiIxFxa5Pxgoh4Zs1M\nEbF3RJzfbPcrImLbceYZlSki5kTExyPiFxGxpHnOqRGxWc1czfyjI+KaJtc9zfp7Qc1MA8/9fPOc\nQ8aZqU2uiPjykN46t2am5jnbRMTZEbG4WY8/i4in1srUTHt4yLJ677gytcy1fkScEBELm666OiLe\nUTnTxhFxSjN/aUSc20F/ttrf7LLX22Sq1OvT5qrR7S2XVae93nabmvT8Tnq95bLqtNdX4/XXda+3\nWVaddnvLTJ32estMNXp95DF7l53eJlONTtfs5cDt7LU+5R6+7wT6cj+MnYHPAi8EXgGsDfwgIv6k\nYqaFwJHADsCOwEXA2RGxTcVMK0XE84G/A66snaVxFeX+0Js2j5fUDBMRTwAuAX4PvBLYBngvsKhm\nLmAnHllGmwK7U16HZ1bM9D7gHZRO2Bo4AjgiIt5dMRPAScBuwP7Ac4ALgAvHeVA4xLR9GRFHAu+m\nvBZfACwFzo+IdWplauZfTFmPXXX8dJnWA7YHPgw8D9gbeDZwduVcANcC76JsX38JLKC89zyxYiag\n7NRT3hNvGWOW1c31fR7d82+smSkinkHZ1n8F7AI8FzgGWF4rE2W5bMYjy+ggYAXwrTFmapPrU8Ae\nwH6Unv8UcEJE7Fkx09mUb0x+LaUjbqJ0/Dj3/Ubub1bo9Tb7wDV6fVSuGt3eZll13eutj2E67vW2\nubrs9Tavvxq93mZZdd3tbTJ13ettMtXo9WmP2Svtq48aR6jR6ZqtMtPHLH9Q3nD2qp1jSK6Nmmwv\nqZ1lINfdwIE9yLEBZcf05cCPgOMq5zkauKL2chnI9DHgx7VztMh5PHBd5QzfA744MO1bwGkVM80F\nHgReNTD958BHKmVapS+BW4HDJv38OGAZ8PpamSbNe1ozf9vay2nIc3YCHgae2rNcj22e97KamYCn\nUA52tgFuBA6pvQ6BLwNndZmjRaavAaf2KdOQ53wXuKB2LuCXwPsHpnXWp4OZgGc107aeNC2AO4CD\nOlxWq+xv9qDXp9wHrtXro3JNek6n3d4yU9e9PjRTD3p92LZeu9eHZara66uxXXXa7VMsq9q9/qhM\nfen15t9decxeu9OHZZo0rVqn+5g9D8+4VU1PoHz6dE/tILDyUvJ9KWcX/HftPMB/AN/LzItqB5nk\nWc1lMb+JiNMjYvPKeV4L/Dwizmwu57kiIt5WOdOjRMTalLNJT6oc5VJgt4h4FkBEbEc5S2Wsl0GP\nMAd4DOWM6cmWUfls7gkR8XTKWRc/nJiWmfcCPwNeVCvXDDHR8YtrB5nQvB7fQclU7UqGiAjgNODY\nzLymVo4p7Nr06fyIODEi/rRWkGY5vQa4PiLOa3L9NCJeVyvToIjYGHg18KXaWSg9v1dEPBkgIl5G\nOcg+v1KedSkdsLLjM3Pi5y47/lH7mz3p9V7tA0/SJlfX3T5tpkq9vkqmnvT6VMuqZq8Pvv760uuj\ntqsa3T4sU+1eH8xUvdcHjtkv7UOn93AcQbOMA7eqonnTPh74SWZWvU9qRDwnIu6jvOGcCOydmfMr\nZ9qXcunJUTVzDPgpcADllgQHA08H/isi1q+YaUvg7ylnJu8BfA74TET8bcVMg/YGHg+cWjnHx4Bv\nAPMj4gHgcuD4zPx6rUCZuYSyc/PBiNis2el5E2Unq8tbJUxnU8oO6h0D0+9o5mmIiFiXss2d0azn\n2nle0/T8cuBQYPfMrDlg8j7ggcw8oWKGYb4PvJlypccRwEuBc5v37Bo2plx9ciTlQ6bdge8AZ0XE\nzpUyDToAuJeSq7b3ANcANzc9fy7wrsy8pFKe+ZRLST8aEU+IiHWay1mfSkcdP8X+ZtVe79M+8GRt\ncnXd7dNlqtXr02Sq2uvT5KrW61Nkqt7rLV+DB9Bht0+TqVqvT5GpWq9Pccx+LRU7vY/jCJqd5tQO\noDXWicCfU874q20+sB1lcG0f4LSI2KVW6Ua5Mf/xwCsy88EaGYbJzMmf7F4VEfOA3wKvp1yGVcNa\nwLzM/GDz85UR8RzKwPJXKmUadBDw/cy8vXKON1Duj7Uv5Z5i2wOfjohbM7PmsnoTcDLlXnAPAVcA\nZ1DuFaUZKCLmAN+k7ES/s3KcCRdRen4j4O3ANyPiBZl5V9dBImJH4BDK/SJ7JTMn34f76oj4JfAb\nYFfKLXu6NnGCwXcz8zPNf/8iIl5M6fmLK2QadCBwemY+UDsIZbt6IbAn5XLtXYATm57v/OqdGPsE\nNAAACJ5JREFUzHwoyv0+T6KcrfUQcCFl4KGrDwP6tL85oY+ZYESuSt0+XaZavb5Kpp70+tBlVbnX\nh2XqQ6+3eQ123e1TZarZ66tkqtzrQ4/Zx/xvjtKrcQTNXp5xq85FxAmUS092zczbaufJzIcy84bM\n/J/MfD/lMqtDK0baEXgScEVEPBgRD1I+HT80Ih6oeObTo2Tm74DrgLF+Y+cIt1E+hZ7sGmCLCllW\nERFbUG7s/8XaWYBjgY9l5jcz8+rM/CrlCw6qntWdmTdm5ssoN/DfPDP/AlgHuKFmrklup+yIbjIw\nfZNmniaZdGC/ObBHH862BcjMZU3Pz8vMt1MONN5aKc5LKB2/cFLHPw04LiL6st0D5fUJ3EW9nr+L\nsq562fPN2WFb0YPbJETEXOBfgcMz89zMvCozT6RcafGPtXI1+1Y7UA5qN8vMV1MG2sa+rU+zv1mt\n1/u2DzxhVK4a3T4qU41enyZT1V5fne2qq16fJlPVXm+zrLru9qky1ez16ZZTrV6f5pi9Wqf3cBxB\ns5QDt+pU8ybwOsqXB9xUO88U1qLcv6eWCynfrro95RO87Sg3oT8d2K65j1B1EbEBZaev5oHHJZRv\nN57s2ZQzgfvgIMplOjXvIzthPcqXiUy2gp68DzQHYHdExIaU23F8t3YmWHmAczuw28S0iHgc5eyH\nS2vlGtCXTpg4sN8S2C0zF1WONJ2aPX8asC2P9Pt2lC/VOJay7fdGcwXIE6nU881VJ5exas9vRT96\n/q3A5Zl5Ve0glG/+XptVe/5hetDzmXlfZt4d5T7rOzHmjp9uf7NWr6/mPnBnvT4qV41u/wOPF8ba\n6yMyVev11V1WXfT6iNdftV5fjWXVWbePyFSl19sup657fYi1gHV7tq8+VQ/1Yl9dM5e3SpilmvuO\nPpNHLlnYMsqXEd2TmQsrZToReCOwF7A0IiY+FftdZi6vlOnfKPd+uonyjbT7U85u3aNGHoDMXEq5\nlH2liFgK3J0Vv8QmIj4BfI+yU/UU4MPAg5Rvhq3lU8AlEXEUcCblDfptlEvmqmrOjD4AOCUzV1SO\nA2XdfSAibgauBnYADqPymWIRsQelp66lfNnCsZTt/5QOM4zqy+Mpy+7XwALgGOBm4OxamZoB7i0o\nr8UAtm62udszc/AeX2PPRDkA/DblA6c9gbUndfw947zty4hcdwPvB85pMm4EvBt4MmUgovNMzTa1\naOD5D1LW3fXjyjQqV/M4mrIeb2+e93HKlRVj+xKUFsvqE8DXI+JiymW9f0XZxl5aMdPEQeE+lB7t\nRIte+DHwyYh4D+W9elfKvS3/oWKmfYA7KftZ21L69KzM/OHQP/jHydRmf7PTXm+TqVKvT5urGbTt\ntNtbZFqPjnt9VKZmMLvzXm+xrNan415v+fqr0eutjkO77PYW29V9Xfd6y66q0eujjtlr7KtPm6lG\np2sWy0wfs/BBKY0VlE/kJj9OrphpWJ6HgTdXzPQlymUdyyg7ND8AXl57/Q3JeRFwXOUMX6O8AS6j\nvEGdATy9B8vm1cAvgPspA5IH1c7U5Nq92b6fWTtLk2d94DjgRmApcD1l8H1O5Vx/A/y62a5uAT4N\nPLbjDCP7EvgQ5eyZ+ykHO2Ndr6MyAW+ZYv4/18hEuSR0cN7Ez7vUWlaUsx6+TfkijWVNh30H2KH2\nNjXw/BuAQ2pu68Bc4LzmvXB5k+lzwJNqLyvKh2DXNd11BbBnDzK9HVjSZV+16IWNKfcdXNgsq18B\nh1bO9B7KPsNyyvvPhxjz+84UeVbZ36TDXm+TiTq9Pm0uSrcPzhtrt7fI1Hmvt92mBn5n7L3eYll1\n3uur8fo7gG57vW2uzrq9ZS902ustM9Xo9ZHH7HS/rz5tJip0uo/Z+4hMz9qWJEmSJEmSpD6pfs8r\nSZIkSZIkSdKjOXArSZIkSZIkST3jwK0kSZIkSZIk9YwDt5IkSZIkSZLUMw7cSpIkSZIkSVLPOHAr\nSZIkSZIkST3jwK0kSZIkSZIk9YwDt5IkSZIkSZLUMw7cSpIkSZIkSVLPOHArSZIkrYaI+FFEHFc7\nhyRJkmY3B24lSZIkSZIkqWccuJUkSZIkSZKknnHgVpIkSZpCRKwXEadFxH0RcUtEHD4w/00RcVlE\n3BsRt0XEVyPiSZPmXz/kd7aPiBURsWVX/x+SJEmaeRy4lSRJkqb2SWBn4LXAHsCuwA6T5s8BPgBs\nC7wOeBpwyqT5JwMHDvzNA4EfZ+YNY0ksSZKkWSEys3YGSZIkqXciYn3gbmC/zDyrmbYhcDPwhcw8\nfMjv7AT8DHhsZt4fEZsBvwVenJk/j4g5wK3A4Zl5elf/L5IkSZp5PONWkiRJGu4ZwNrAvIkJmbkI\nuHbi54jYMSLOiYjfRsS9wH82s7Zonn8bcC5wUDN9L2Ad4FtjTy9JkqQZzYFbSZIk6Q8QEesB5wGL\ngf2AnYC9m9nrTHrql4B9I2Jd4ADgG5m5vMOokiRJmoEcuJUkSZKG+w3wEPDCiQnNrRK2an7cGngi\ncFRmXpKZ1wGbDPk75wJLgXcCrwJOGmdoSZIkzQ5zageQJEmS+igzl0bEScAnIuIe4E7gX4CHm6fc\nBDwAHBIRnweeS/missG/syIiTgU+ClyXmfMGnyNJkiQN8oxbSZIkaWr/BFwMnAP8oPnvywEy8y7g\nLcA+wNXAEcB7p/g7J1Fun3DymPNKkiRplojMrJ1BkiRJmtUiYmfgAmDzzLyzdh5JkiT1nwO3kiRJ\n0phExDrAxsApwK2Z+ea6iSRJkjRTeKsESZIkaXzeCCwAHgccWTeKJEmSZhLPuJUkSZIkSZKknvGM\nW0mSJEmSJEnqGQduJUmSJEmSJKlnHLiVJEmSJEmSpJ5x4FaSJEmSJEmSesaBW0mSJEmSJEnqGQdu\nJUmSJEmSJKlnHLiVJEmSJEmSpJ5x4FaSJEmSJEmSesaBW0mSJEmSJEnqmf8HQpX2NTkzuEIAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21597773390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "# 天数\n",
    "day_range = range(1,len(df_user['day']) + 1, 1)\n",
    "# 设置图片大小\n",
    "plt.figure(figsize=(14,10))\n",
    "\n",
    "plt.bar(df_user['day'], df_user['user_num'], bar_width, \n",
    "        alpha=opacity, color='c', label='user')\n",
    "plt.bar(df_item['day']+bar_width, df_item['item_num'], \n",
    "        bar_width, alpha=opacity, color='g', label='item')\n",
    "plt.bar(df_ui['day']+bar_width*2, df_ui['user_item_num'], \n",
    "        bar_width, alpha=opacity, color='m', label='user_item')\n",
    "\n",
    "plt.xlabel('day')\n",
    "plt.ylabel('number')\n",
    "plt.title('March Purchase Table')\n",
    "plt.xticks(df_user['day'] + bar_width * 3 / 2., day_range)\n",
    "# plt.ylim(0, 80)\n",
    "plt.tight_layout() \n",
    "plt.legend(prop={'size':9})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：3月份14,15,16不知名节日，造成购物大井喷，总体来看，购物记录多于2月份"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2016年4月"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration is stopped\n"
     ]
    }
   ],
   "source": [
    "df_ac = get_from_action_data(fname=ACTION_201604_FILE)\n",
    "\n",
    "# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天\n",
    "df_ac['time'] = pd.to_datetime(df_ac['time']).apply(lambda x: x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_user = df_ac.groupby('time')['user_id'].nunique()\n",
    "df_user = df_user.to_frame().reset_index()\n",
    "df_user.columns = ['day', 'user_num']\n",
    "\n",
    "df_item = df_ac.groupby('time')['sku_id'].nunique()\n",
    "df_item = df_item.to_frame().reset_index()\n",
    "df_item.columns = ['day', 'item_num']\n",
    "\n",
    "df_ui = df_ac.groupby('time', as_index=False).size()\n",
    "df_ui = df_ui.to_frame().reset_index()\n",
    "df_ui.columns = ['day', 'user_item_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x215d4d80da0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qmZu3AfdX1Tm9g4zcl4DP0UYiPAV4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\nsXArSZIkSZIkSRPmd+nyDqjpY5AmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215be40f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "# 天数\n",
    "day_range = range(1,len(df_user['day']) + 1, 1)\n",
    "# 设置图片大小\n",
    "plt.figure(figsize=(14,10))\n",
    "\n",
    "plt.bar(df_user['day'], df_user['user_num'], bar_width, \n",
    "        alpha=opacity, color='c', label='user')\n",
    "plt.bar(df_item['day']+bar_width, df_item['item_num'], \n",
    "        bar_width, alpha=opacity, color='g', label='item')\n",
    "plt.bar(df_ui['day']+bar_width*2, df_ui['user_item_num'], \n",
    "        bar_width, alpha=opacity, color='m', label='user_item')\n",
    "\n",
    "plt.xlabel('day')\n",
    "plt.ylabel('number')\n",
    "plt.title('April Purchase Table')\n",
    "plt.xticks(df_user['day'] + bar_width * 3 / 2., day_range)\n",
    "# plt.ylim(0, 80)\n",
    "plt.tight_layout() \n",
    "plt.legend(prop={'size':9})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：一脸懵逼中...可能又有啥节日？ 还是说每个月中旬都有较强的购物欲望？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 商品类别销售统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 周一到周日各商品类别销售情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从行为记录中提取商品类别数据\n",
    "def get_from_action_data(fname, chunk_size=50000):\n",
    "    reader = pd.read_csv(fname, header=0, iterator=True)\n",
    "    chunks = []\n",
    "    loop = True\n",
    "    while loop:\n",
    "        try:\n",
    "            chunk = reader.get_chunk(chunk_size)[\n",
    "                [\"cate\", \"brand\", \"type\", \"time\"]]\n",
    "            chunks.append(chunk)\n",
    "        except StopIteration:\n",
    "            loop = False\n",
    "            print(\"Iteration is stopped\")\n",
    "\n",
    "    df_ac = pd.concat(chunks, ignore_index=True)\n",
    "    # type=4,为购买\n",
    "    df_ac = df_ac[df_ac['type'] == 4]\n",
    "\n",
    "    return df_ac[[\"cate\", \"brand\", \"type\", \"time\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration is stopped\n",
      "Iteration is stopped\n",
      "Iteration is stopped\n"
     ]
    }
   ],
   "source": [
    "df_ac = []\n",
    "df_ac.append(get_from_action_data(fname=ACTION_201602_FILE))\n",
    "df_ac.append(get_from_action_data(fname=ACTION_201603_FILE))\n",
    "df_ac.append(get_from_action_data(fname=ACTION_201604_FILE))\n",
    "df_ac = pd.concat(df_ac, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将time字段转换为datetime类型\n",
    "df_ac['time'] = pd.to_datetime(df_ac['time'])\n",
    "\n",
    "# 使用lambda匿名函数将时间time转换为星期(周一为1, 周日为７)\n",
    "df_ac['time'] = df_ac['time'].apply(lambda x: x.weekday() + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cate</th>\n",
       "      <th>brand</th>\n",
       "      <th>type</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>306</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>174</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>306</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cate  brand  type  time\n",
       "0     9    306     4     1\n",
       "1     4    174     4     1\n",
       "2     5     78     4     1\n",
       "3     5     78     4     1\n",
       "4     4    306     4     1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cate</th>\n",
       "      <th>brand</th>\n",
       "      <th>type</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9326</td>\n",
       "      <td>9326</td>\n",
       "      <td>9326</td>\n",
       "      <td>9326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8138</td>\n",
       "      <td>8138</td>\n",
       "      <td>8138</td>\n",
       "      <td>8138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6982</td>\n",
       "      <td>6982</td>\n",
       "      <td>6982</td>\n",
       "      <td>6982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6214</td>\n",
       "      <td>6214</td>\n",
       "      <td>6214</td>\n",
       "      <td>6214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13281</td>\n",
       "      <td>13281</td>\n",
       "      <td>13281</td>\n",
       "      <td>13281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4104</td>\n",
       "      <td>4104</td>\n",
       "      <td>4104</td>\n",
       "      <td>4104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cate  brand   type   time\n",
       "cate                            \n",
       "4      9326   9326   9326   9326\n",
       "5      8138   8138   8138   8138\n",
       "6      6982   6982   6982   6982\n",
       "7      6214   6214   6214   6214\n",
       "8     13281  13281  13281  13281\n",
       "9      4104   4104   4104   4104\n",
       "10      189    189    189    189\n",
       "11       18     18     18     18"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察有几个类别商品\n",
    "df_ac.groupby(df_ac['cate']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x215b4de5eb8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9aQ466CBuvPHG2hfYDwx6JEmSJElSv2kGxta7iF669tpree655zjvvPMAaG1t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\nANCJ/wPvh46RMJWzqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x215bcd6cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 周一到周日每天购买商品类别数量统计\n",
    "df_product = df_ac['brand'].groupby([df_ac['time'],df_ac['cate']]).count()\n",
    "df_product=df_product.unstack()\n",
    "df_product.plot(kind='bar',title='Cate Purchase Table in a Week',figsize=(14,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：星期二买类别8的最多，星期天最少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 每月各类商品销售情况（只关注商品8）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2016年2，3，4月"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration is stopped\n",
      "Iteration is stopped\n",
      "Iteration is stopped\n"
     ]
    }
   ],
   "source": [
    "df_ac2 = get_from_action_data(fname=ACTION_201602_FILE)\n",
    "\n",
    "# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天\n",
    "df_ac2['time'] = pd.to_datetime(df_ac2['time']).apply(lambda x: x.day)\n",
    "df_ac3 = get_from_action_data(fname=ACTION_201603_FILE)\n",
    "\n",
    "# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天\n",
    "df_ac3['time'] = pd.to_datetime(df_ac3['time']).apply(lambda x: x.day)\n",
    "df_ac4 = get_from_action_data(fname=ACTION_201604_FILE)\n",
    "\n",
    "# 将time字段转换为datetime类型并使用lambda匿名函数将时间time转换为天\n",
    "df_ac4['time'] = pd.to_datetime(df_ac4['time']).apply(lambda x: x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_cate2 = df_ac2[df_ac2['cate']==8]\n",
    "dc_cate2 = dc_cate2['brand'].groupby(dc_cate2['time']).count()\n",
    "dc_cate2 = dc_cate2.to_frame().reset_index()\n",
    "dc_cate2.columns = ['day', 'product_num']\n",
    "\n",
    "dc_cate3 = df_ac3[df_ac3['cate']==8]\n",
    "dc_cate3 = dc_cate3['brand'].groupby(dc_cate3['time']).count()\n",
    "dc_cate3 = dc_cate3.to_frame().reset_index()\n",
    "dc_cate3.columns = ['day', 'product_num']\n",
    "\n",
    "dc_cate4 = df_ac4[df_ac4['cate']==8]\n",
    "dc_cate4 = dc_cate4['brand'].groupby(dc_cate4['time']).count()\n",
    "dc_cate4 = dc_cate4.to_frame().reset_index()\n",
    "dc_cate4.columns = ['day', 'product_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x215979c81d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SAGCoaZq0bbvr2mAwyPr6+ognAgAAYFYJtwCQ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\nSZIkSS3z/4nTJTrWyxqcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2159da8e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形宽度\n",
    "bar_width = 0.2\n",
    "# 透明度\n",
    "opacity = 0.4\n",
    "# 天数\n",
    "day_range = range(1,len(dc_cate3['day']) + 1, 1)\n",
    "# 设置图片大小\n",
    "plt.figure(figsize=(14,10))\n",
    "\n",
    "plt.bar(dc_cate2['day'], dc_cate2['product_num'], bar_width, \n",
    "        alpha=opacity, color='c', label='February')\n",
    "plt.bar(dc_cate3['day']+bar_width, dc_cate3['product_num'], \n",
    "        bar_width, alpha=opacity, color='g', label='March')\n",
    "plt.bar(dc_cate4['day']+bar_width*2, dc_cate4['product_num'], \n",
    "        bar_width, alpha=opacity, color='m', label='April')\n",
    "\n",
    "plt.xlabel('day')\n",
    "plt.ylabel('number')\n",
    "plt.title('Cate-8 Purchase Table')\n",
    "plt.xticks(dc_cate3['day'] + bar_width * 3 / 2., day_range)\n",
    "# plt.ylim(0, 80)\n",
    "plt.tight_layout() \n",
    "plt.legend(prop={'size':9})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：2月份对类别8商品的购买普遍偏低，3，4月份普遍偏高，3月15日购买极其多！可以对比3月份的销售记录，发现类别8将近占了3月15日总销售的一半！同时发现，3,4月份类别8销售记录在前半个月特别相似，除了4月8号，9号和3月15号。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看特定用户对特定商品的的轨迹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def spec_ui_action_data(fname, user_id, item_id, chunk_size=100000):\n",
    "    reader = pd.read_csv(fname, header=0, iterator=True)\n",
    "    chunks = []\n",
    "    loop = True\n",
    "    while loop:\n",
    "        try:\n",
    "            chunk = reader.get_chunk(chunk_size)[\n",
    "                [\"user_id\", \"sku_id\", \"type\", \"time\"]]\n",
    "            chunks.append(chunk)\n",
    "        except StopIteration:\n",
    "            loop = False\n",
    "            print(\"Iteration is stopped\")\n",
    "\n",
    "    df_ac = pd.concat(chunks, ignore_index=True)\n",
    "    df_ac = df_ac[(df_ac['user_id'] == user_id) & (df_ac['sku_id'] == item_id)]\n",
    "\n",
    "    return df_ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def explore_user_item_via_time():\n",
    "    user_id = 266079\n",
    "    item_id = 138778\n",
    "    df_ac = []\n",
    "    df_ac.append(spec_ui_action_data(ACTION_201602_FILE, user_id, item_id))\n",
    "    df_ac.append(spec_ui_action_data(ACTION_201603_FILE, user_id, item_id))\n",
    "    df_ac.append(spec_ui_action_data(ACTION_201604_FILE, user_id, item_id))\n",
    "    df_ac = pd.concat(df_ac, ignore_index=False)\n",
    "    print(df_ac.sort_values(by='time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration is stopped\n",
      "Iteration is stopped\n",
      "Iteration is stopped\n",
      "    user_id  sku_id  type                 time\n",
      "0    266079  138778     1  2016-01-31 23:59:02\n",
      "1    266079  138778     6  2016-01-31 23:59:03\n",
      "15   266079  138778     6  2016-01-31 23:59:40\n"
     ]
    }
   ],
   "source": [
    "explore_user_item_via_time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
